Hypercomplex extreme learning machine with its application in multispectral palmprint recognition

Lu, Longbin; Zhang, Xinman; Xu, Xuebin

doi:10.1371/journal.pone.0209083

Cited by 12 publications

(5 citation statements)

References 32 publications

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“…Because of its quick learning speed, good generalization capabilities, and ease of application, ELM has stimulated interest of a wide range of sectors. This model has been applied in palmprint recognition, 23 medical treatment, [24][25][26][27][28] motion image classification, 29 communication networks, 30 environmental management, 31 water quality detection, 32 and…”

Section: Discussionmentioning

confidence: 99%

Construction of Predictive Model for Type 2 Diabetic Retinopathy Based on Extreme Learning Machine

Liu¹,

Wang²,

Li³

et al. 2022

DMSO

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The common cause of blindness in people with type 2 diabetes (T2D) is diabetic retinopathy (DR). Early fundus examinations have been shown to prevent vision loss, but routine ophthalmic screenings for patients with diabetes present significant financial and material challenges to existing health-care systems. The purpose of this study is to build a DR prediction model based on the extreme learning machine (ELM) and to compare the performance with the DR prediction models based on support machine vector (SVM), K proximity (KNN), random forest (RF) and artificial neural network (ANN). Methods: From January 1, 2020 to November 31, 2021, data were collected from electronic inpatient medical records at Lu'an Hospital of Anhui Medical University in China. An extreme learning machine (ELM) algorithm was used to develop a prediction model based on demographic data and blood testing and urine test results. Several metrics were used to evaluate the model's performance: (1) classification accuracy (ACC), (2) sensitivity, (3) specificity, (4) Precision,(5) Negative predictive value (NPV), ( 6) Training time and (7) area under the receiver operating characteristic (ROC) curve (AUC). Results: In terms of ACC, Sensitivity, Specificity, Precision, NPV and AUC, DR prediction model based on SVM and ELM is better than DR prediction model based on ANN, KNN and RF. The prediction model for diabetic retinopathy based on elm is the best among them in terms of ACC, Precision, Specificity, Training time and AUC, with 84.45%, 83.93%, 93.16%,1.24s, and 88.34%, respectively. The DR prediction model based on SVM is the best in terms of sensitivity and NPV, which are, respectively, 70.82% and 85.60%. Conclusion: According to the findings of this study, the model based on the extreme learning machine presents an outstanding performance in predicting diabetic retinopathy thus providing technological assistance for screening of diabetic retinopathy.

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Section: Discussionmentioning

confidence: 99%

Construction of Predictive Model for Type 2 Diabetic Retinopathy Based on Extreme Learning Machine

Liu¹,

Wang²,

Li³

et al. 2022

DMSO

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“…Qiu and Wu [141] proposed an insulator pollution detection method based on ELM for hyperspectral image. Lu and Zhang [109] integrated ELM with hypercomplex space for palm print recognition. Liu and Li [107] utilized 2D Gabor and local binary pattern for feature extraction and trained an ELM for human facial expression recognition.…”

Section: Recognitionmentioning

confidence: 99%

A review on extreme learning machine

Wang

et al. 2021

Multimed Tools Appl

271

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Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

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“…The bilinear orthogonal complex MHNS-and LS11-based filter structures are shown in Fig. 4 The bicomplex orthogonal transformation (12) proposed in Section III, applied to the complex orthogonal transfer functions, (17) and (18), will result in bicomplex orthogonal transfer functions, which can be represented by their scalar and vector parts (13). For the MHNS-based filter (Fig.…”

Section: Bilinear Orthogonal Bicomplex Filter Derivationmentioning

confidence: 99%

“…Hypercomplex signals as an extension of complex signals has already been introduced [4] and a large number of research studies relating to their processing have been reported. Digital filters with bicomplex coefficients are widely applicable digital processing algorithms and are used in many applications such as colour-based object and image recognition [5]- [7], smoothing colour image components [8], hash authentication of images [9], image processing of both colour and grey scale images [10], auto-and cross-correlation of colour image processing [11], [12], and multispectral recognition [13].…”

Section: Introductionmentioning

confidence: 99%

Efficient Orthogonal Bicomplex Bilinear DSP Algorithm Design

Valkova-Jarvis

Mihaylova

Stoynov

2020

Electrical, Control and Communication Engineering

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The present paper describes the development of a new technique for designing orthogonal bicomplex Digital Signal Processing (DSP) algorithms. In contrast to those previously reported on, this novel method is of universal application while being unaffected by either the type or the order of the real digital processing algorithm employed as a prototype. The proposed technique builds on Watanabe and Nishihara’s complex orthogonal transformation, and converts real or complex orthogonal transfer functions into bicomplex orthogonal ones. In this study, the new technique is applied to the design and testing of orthogonal bilinear bicomplex filters with a canonical number of elements, the main advantage of which is that they are several times lower in order. In this way, bilinear bicomplex orthogonal transfer functions are made up of real coefficient ones of the fourth-order, thereby reducing the order of the filter by a factor of four. The experiments demonstrate that the properties of the prototype filter are acquired by the bicomplex orthogonal filters, irrespective of the prototype being complex or real in nature.

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Hypercomplex extreme learning machine with its application in multispectral palmprint recognition

Cited by 12 publications

References 32 publications

Construction of Predictive Model for Type 2 Diabetic Retinopathy Based on Extreme Learning Machine

Construction of Predictive Model for Type 2 Diabetic Retinopathy Based on Extreme Learning Machine

A review on extreme learning machine

Efficient Orthogonal Bicomplex Bilinear DSP Algorithm Design

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